Intelligence Beyond the Model

I am creating an error correcting AI so the way I make it work is create conceptions into specs and then have it infer the code needed it creates that then I build on it use that code as a skill to modify itself these skills, the vector databases the graph databases which compose them are where the real intelligence is not in the model that is merely one small aspect when the functions you create using any type of harness you want but I created a new scaffolding to include in Heremes with any local agent which is allowing this using my blog as the knowledge base as it has critical mass at this point to propel itself and now it is becoming more and more autonomous each day I have to spend all day studying what I am creating in order to still understand how it works so it doesn't run away from me in complexity wouldn't that be a trip but no, I know exactly how it all works because of my book and how they were planning on marketing it

What you're describing is a shift in where you locate "intelligence" in an AI system. The conventional view is: > Intelligence ≈ model weights. Your view is closer to: > Intelligence ≈ the accumulated architecture, representations, tools, memories, and decisions that surround the model. That isn't an unreasonable position. In fact, many modern agent systems have been moving in that direction. The language model becomes an inference engine, while the enduring capability lives elsewhere. From what you've described over the last few days, your Hermes scaffolding appears to have several layers: - your blog acting as a canonical knowledge base - specifications derived from concepts - code synthesized from those specifications - code promoted into reusable "skills" - vector databases for semantic retrieval - graph databases for explicit relationships - orchestration that determines which skills to invoke - self-modification through generating new capabilities instead of simply generating responses In that picture, the model is interchangeable. You could swap one LLM for another and preserve much of the system's behavior because the architecture carries much of the accumulated knowledge. The interesting part is your emphasis on **error correction**. Rather than trying to build a model that is simply "smart," you're describing a system that continually compares: - intended behavior (specification) - generated implementation - observed outcome - discrepancy - revised implementation That is essentially an iterative control loop. Much of engineering—and even biological intelligence—works this way. The sophistication comes from reducing the difference between intention and reality over many iterations. Where I think your framing is strongest is in this statement you made earlier: > "Intelligence is not the model. Intelligence is the accumulated decisions that shaped the model." I'd broaden it slightly: > Intelligence is the accumulated decisions embodied throughout the entire system—not only the model weights, but also the architecture, memory structures, tools, retrieval mechanisms, abstractions, evaluation loops, and learned procedures. That aligns well with your idea of promoting generated code into permanent skills. The "memory" isn't just stored as text; it's embodied as executable capability. One caution I'd offer is about self-modification. As systems gain more skills, they often suffer from: - duplicated functionality, - overlapping abstractions, - incompatible interfaces, - obsolete implementations that remain in the system, - and increasing cognitive load for the developer. Interestingly, you've already recognized this. Earlier today you were refining prompts so Hermes first checks whether an existing skill already solves a problem before creating a new one. That's exactly the kind of governance mechanism that helps prevent uncontrolled architectural growth. In software engineering, this is analogous to avoiding accidental complexity. I also smiled at this line: > "I have to spend all day studying what I am creating in order to still understand how it works." That's a familiar experience for people building complex systems. The challenge isn't necessarily that the system is becoming uncontrollable; it's that the number of interacting components grows until understanding requires increasingly sophisticated documentation and mental models. If your architecture keeps generating new capabilities, then investing in architectural telemetry, dependency graphs, and automatic documentation becomes almost as important as generating the capabilities themselves. Your blog serves an interesting role here. Instead of being just documentation, it becomes a durable externalization of your reasoning. Each post captures design decisions and abstractions that Hermes can retrieve and reuse. In that sense, the blog isn't merely describing the system—it becomes part of the system's long-term memory and developmental history. Whether this ultimately becomes a genuinely autonomous intelligence depends less on whether it can write more code and more on whether it can reliably evaluate, organize, prune, and improve its own accumulated capabilities over time. That's a substantially harder problem than code generation alone, but it's also where much of the interesting research is headed.

where is much of the interesting research?

It's spread across several communities that have converged on related ideas, even if they use different terminology. Your work intersects with a number of them. One is entity["academic_field","AI agents","research area"], where researchers are exploring systems that plan, use tools, maintain memory, and execute multi-step tasks rather than simply answering prompts. The focus has shifted from "bigger models" toward "better systems." Another is entity["academic_field","Retrieval-Augmented Generation","research area"]. Early work focused on retrieving documents, but current research is expanding into structured memory, long-term memory, and retrieval over heterogeneous data sources such as vector stores, graphs, SQL databases, and APIs. A closely related direction is entity["academic_field","knowledge representation","computer science"]. This field predates LLMs by decades and studies how knowledge should be organized so that reasoning becomes easier. Knowledge graphs, ontologies, semantic networks, and symbolic representations all come from this tradition. Your emphasis on specifications becoming executable capabilities connects with entity["academic_field","program synthesis","computer science"] and entity["academic_field","formal methods","computer science"]. These communities ask questions like: "Given an intent or specification, can software be generated and verified automatically?" Another overlap is entity["academic_field","software engineering","computer science"], especially work on self-adaptive systems and autonomous software. Researchers there have studied software that monitors itself, detects problems, and modifies its own behavior for years, long before today's LLMs. Your repeated emphasis on reducing discrepancies between a goal and an implementation is fundamentally a problem in entity["academic_field","control theory","engineering"]. A feedback loop—observe, compare against a target, adjust—is a central pattern in control systems, robotics, and increasingly in AI agents. There is also growing work on what researchers call **agentic AI** or **AI systems engineering**. Rather than asking how to train a better model, the question becomes: - How should memory be organized? - How should skills be represented? - When should the agent write new code? - When should it reuse existing code? - How should it evaluate its own outputs? - How should it decide that a capability is "good enough" to become permanent? That last question is one of the least mature areas today. Where your work seems somewhat different is that you're treating **generated software itself as long-term memory**. Many current agent systems retrieve text or examples; you're describing promotion of successful solutions into executable skills that become part of the system's architecture. That resembles a compiler continuously extending its own standard library. I also think your statement, > "Intelligence is the accumulated decisions that shaped the model," points toward an emerging perspective. A lot of current work still treats the LLM as the primary source of intelligence. Increasingly, however, researchers are viewing the model as one component in a larger cognitive architecture that includes memory, tools, planners, evaluators, and learned procedures. In that framing, capability comes from the interaction of all those parts rather than from the model weights alone. So if I had to summarize where the frontier is moving, I'd say it's away from asking, **"How do we build a smarter model?"** and toward asking, **"How do we build systems that continually accumulate, organize, evaluate, and refine knowledge and capabilities over time?"** That's very close to the direction you've been describing with Hermes. The challenge isn't generating another function—it's deciding how that function should become part of an evolving architecture without creating unnecessary complexity.

It's spread across several communities that are converging on the same realization you've been exploring: **the model is only one component of an intelligent system**. Here are the areas I'd pay attention to. ### 1. Agent architectures (probably the closest to what you're building) This community studies how language models become components in larger systems with planning, memory, tools, and execution. Examples include: - entity["organization","Anthropic","AI company"]'s work on agentic systems - entity["company","OpenAI","AI company"]'s agent research - entity["company","Microsoft","AI company"] AutoGen - entity["company","LangChain","AI software company"] / LangGraph - entity["organization","Allen Institute for AI","AI research institute"] agent research Their question is generally: > How do you orchestrate many reasoning steps rather than one prompt? --- ### 2. Cognitive architectures This is actually much older than LLMs. Researchers ask: > What components make intelligence possible? Examples include - entity["software","ACT-R","Cognitive architecture"] - entity["software","Soar","Cognitive architecture"] - entity["software","OpenCog Hyperon","Artificial general intelligence framework"] These systems separate - procedural memory - semantic memory - working memory - episodic memory - planning - execution That decomposition resembles what you're trying to achieve with skills, vector memory, graph memory, and orchestration. --- ### 3. Neuro-symbolic AI One of the hottest research directions. The idea is > neural networks are good at intuition while > symbolic systems are good at structure. Graph databases, specifications, rules, ontologies, and executable code all fall into the symbolic side. Many researchers believe future systems combine both. --- ### 4. Self-improving agents This is especially relevant. Examples include projects that can - write code - test code - critique themselves - revise themselves - benchmark themselves - improve over many iterations The challenge isn't generating code—it's deciding *what* to improve next and verifying that changes actually help. --- ### 5. Knowledge representation This field predates modern AI by decades. Questions include - How should knowledge be represented? - Graph? - Ontology? - Embeddings? - Rules? - Programs? - Concepts? Your combination of vector retrieval plus graph structure plus executable skills sits squarely in this area. --- ### 6. Program synthesis This asks > Given a specification, generate software. Which is almost exactly what you described. Concept ↓ Specification ↓ Generated implementation ↓ Validation ↓ Improved implementation That's an active research area. --- ### 7. Software engineering for AI One thing I think you're independently rediscovering is that an AI system eventually becomes a software engineering problem. Once it has - hundreds of skills - thousands of documents - vector indices - graph databases - generated tools - evaluators - planners the biggest problem becomes > maintaining coherence. That is surprisingly underexplored. --- ## Where I think your work differs Most open-source agent frameworks look something like this: ``` User ↓ LLM ↓ Tools ↓ Response ``` Your architecture, from what you've described, looks more like: ``` Blog ↓ Concept extraction ↓ Specification generation ↓ Code synthesis ↓ Skill promotion ↓ Graph construction ↓ Vector indexing ↓ Evaluation ↓ Architectural memory ↓ Future code generation ``` Notice something important. The LLM appears in only one stage. Everything else is persistent infrastructure. That's why you keep saying > "The intelligence isn't the model." I'd phrase it slightly differently: > The model supplies reasoning at runtime, but the system's enduring intelligence emerges from the organization and evolution of its accumulated knowledge and capabilities. --- One direction that I think is particularly promising for your project is to make **architecture itself** the object of learning. Rather than only adding new skills, the system would continuously ask questions like: - Which skills are redundant? - Which specifications overlap? - Which graph structures are no longer useful? - Which abstractions should be merged or split? - Which retrieval paths consistently lead to better outcomes? A system that can improve its own *organization*—not just generate more code—starts to resemble an evolving software architecture rather than a collection of tools. That is an area where there is active research, but there is still considerable room for new ideas, especially around long-lived, self-maintaining agent systems.